1

Draft

Modelled Estimates for the Spread and Health Impact of Covid-19 in New

Zealand: Revised Preliminary Report for the NZ Ministry of Health

27 February 2020

Dr Lucy Telfar Barnard, Prof Nick Wilson, Dr Amanda Kvalsvig, Prof Michael Baker (HEIRU,

University of Otago Wellington; contact: michael.baker@otago.ac.nz)

Acknowledgements: We thank our Australian colleagues for their valuable work in providing

modelling data on numbers of infected cases for different scenarios. Nevertheless, the additional

analyses and interpretation in this document are those of the above named authors alone and do not

necessarily represent the views of our Australian colleagues or the New Zealand Ministry of Health.

Key messages

• The future spread and health impact of this new disease are highly uncertain at present as

there is limited evidence about the full spectrum of transmissibility, infection severity and

impact of control measures. This Report therefore can only represent potential future

scenarios rather than predictions.

• If substantially uncontrolled spread of disease occurs in NZ during 2020, our “plan for”

scenario based on the modelling work by our Australian colleagues sees 65% of the

population (3,230,000) contracting a Covid-19 infection, though only 34% of the population

(1,680,000) would probably be symptomatic (typically mild disease). However, these could

well be over-estimates if the disease is less transmissible in NZ than China (at least for

January 2020 in China), if the summer and spring conditions in NZ slow the spread of disease

(noting that other coronavirus infection is typically a winter phenomenon), and if control

measures are more effective than we anticipate. Indeed, our “plan for” scenario is somewhat

towards “worse case” and the most likely outcome for NZ is probably less severe.

• Health service demand is likely to be extremely high in our “plan for” scenario as we

estimate that 336,000 people are likely to require hospitalisation, with a worst day demand of:

7,300 new patients, and 1,450 to 1,700 new intensive care unit (ICU) patients. This high

demand risks overloading the hospital sector (given very limited surge capacity in NZ

hospitals and ICUs at present).

• The peak week in our “plan for” scenario is 21 weeks after virus introduction (for

uncontrolled spread), but successful control measures would postpone the date of the peak by

some weeks to months.

• We estimate likely deaths to be between 12,600 and 33,600 people in our “plan for” scenario.

This range can be considered relatively severe compared with our scenarios where lower

transmissibility is modelled. It is also much more severe than the results of a case study

where we take disease parameters from the largest outbreak outside of China ie, the Diamond

Princess cruise ship outbreak and use these to extrapolate to all of NZ (see Appendix 2). Our

“plan for” mortality estimates could also be over-estimates if treatment options improve and

if a vaccine arrives later in 2020.

• As in previous epidemics and pandemics of infectious respiratory agents, severe disease

burden is likely to fall unequally on Māori, Pacific peoples, and the elderly (see Appendix 3).

2

Background

This report provides numerical estimates and analytical overlay of potential New Zealand Covid-19

scenarios, based on modelling of infected case numbers from McVernon et al and using a NZ

population of 4.96 million (unpublished report provided 21 February 2020).

Method

Graphics of models from McVernon et al were overlaid onto a grid. Data were extracted visually,

and graphed in Excel to extract estimates of total infection and peak day numbers.

Assumptions

This report provides estimates for three potential epidemic scenarios for two levels of infectivity

(R0). All scenarios assume:

• Pre-symptomatic transmission as indicated in a Japanese study1 (though the extent of this is

still very uncertain).

• 48% of infections being asymptomatic (Japanese cruise ship data2);

• 20% of symptomatic infections requiring hospitalisation (ie, not in the “mild” category3). The first, “plan for” infection scenario uses an R0 of 2.2. This was the estimated reproduction

number reported in Li et al 2020,4 which we judge had the least methodological issues of measures to

date.

We also provide numbers for an R0 of 1.5. While an R0 of 1.5 is lower than many early published

estimates, it is consistent with the 2009 H1N1 influenza pandemic for NZ (mid-range out of 3

estimates5) and is more consistent with previous pandemics than values of 2.0 or higher (see

Appendix 2).

For each scenario, we provide:

• Two alternative estimates for ICU demand, one based on a very large Chinese study

indicating that 4.7% of cases are “critical”;3 and the other based on our estimate that 4% of

symptomatic cases are likely to require ICU care.6

• Three alternative estimates for deaths, the first based on the WHO estimated case fatality risk

(CFR) of 2% of symptomatic cases; the second based on our estimate that 34.1% of ICU

admissions end in death6; and the third based on the 21 February 2020 CFR for cases outside

China of 0.75%.7 (The latter was prior to evidence suggestive of uncontrolled disease spread

being plausible in Iran and Italy).

Interpretation and commentary

The large degree of uncertainty in the epidemiology of Covid-19 prevents us from providing narrow

estimates for potential Covid-19 infections.

Our “plan for” scenario (which can be considered to be towards the “worse case” end of the outcome

spectrum) sees 3,230,000 people, or 65% of the population, becoming infected with Covid-19,

though we only expect 34% of the population (1,680,000) to experience symptoms. Nonetheless,

even with these infection numbers, current estimates of the severity of the disease suggest health

services are likely to be over-burdened, with 336,000 people being likely to require hospitalisation,

including a peak demand day of 7,300 additional patients progressing to a stage requiring

hospitalisation. Between 67,000 and 79,000 people are likely to need ICU treatment, with peak day

demand of 1,450 to 1,700 additional ICU patients.

3

Our “plan for” scenario estimates total deaths between 12,600 and 33,600 people. These mortality

estimates could be over-estimates if disease spread is slower (as per R0 = 1.5), and if control

measures are particularly successful; or they could still be under-estimates given hospitals and ICU

overload will probably increase the case fatality risk. However, we note that this range is

substantially higher than our estimate for the NZ mortality burden based on disease parameters for

the largest outbreak outside of China ie, the Diamond Princess cruise ship outbreak (see Appendix

2). Extrapolating from that outbreak, we estimated 6,230 or 6,510 deaths in NZ if the same disease

parameters applied in NZ as applied to this cruise ship. We expect transmission on the cruise ship

was likely to have been very high, but the passengers also likely had a much higher average age than

the NZ population. On the other hand, this cruise ship outbreak has been largely truncated via people

being moved off the ship.

International data suggest that deaths will also typically be in those with co-morbidities: this topic

can be addressed in a future report.

Figure 1: First 5 weeks of epidemic growth, showing all infections regardless of the degree of

symptoms, for R0=1.5, with 48 hrs pre-symptomatic transmission, by effectiveness of control

measures (from McVernon et al)

4

Figure 2: Epidemic curves showing ALL infections regardless of the degree of symptoms, for

R0=1.5 and R0=2.2, with 48 hrs pre-symptomatic transmission, by effectiveness of control

measures (from McVernon et al)

5

Table 1: Estimates for Covid-19 spread in NZ, 48 hrs pre-symptomatic transmission

R0 1.5* 2.2

Infection control effectiveness

25% 0% 50% 25% (“plan for” scenario)

0%

Peak day Week 47 Week 33 Week 27 Week 21 Week 17

All infections

Total 1,490,000 2,370,000 2,630,000 3,230,000 3,560,000

Proportion of NZ population (%)

30% 48% 53% 65% 72%

Worst day 13,500 31,000 42,000 70,000 96,000

Symptomatic cases

Total 773,000 1,230,000 1,370,000 1,680,000 1,850,000

Worst day 7,000 16,000 22,000 36,000 50,000

Severe cases likely to require hospitalisation

Total 155,000 246,000 273,000 336,000 370,000

Worst day 1,400 3,200 4,300 7,300 10,000

Cases likely to require ICU

Total WHO (4.7%) Total UoO (4%)

36,000 31,000

58,000 49,000

64,000 55,000

79,000 67,000

87,000 74,000

Peak WHO (4.7%) Peak UoO (4%)

330 280

800 640

1,000 860

1,700 1,450

2,350 2,000

Deaths (CFR)

Total WHO (2%) Total (ICU x 34%) Total (0.75)%

15,500 10,500 5,800

24,600 16,800 9,200

27,300 18,630 10,200

33,600 23,000 12,600

37,000 25,000 14,000

Peak WHO (2%) Peak (ICU x 34%) Total (O.75%)

140 100 50

320 220 120

430 290 160

730 500 270

1,000 680 380

* Figures for R0=1.5 at 50% infection control are not precisely ascertainable from currently available graphical output from the modelling work by our Australian colleagues, more detail can potentially be made available in a subsequent report.

6

Limitations of this analysis

1. There is a high degree of uncertainty around all aspects of Covid-19 epidemiology. The R0

may well be lower than our 2.2 “plan for” scenario value or it may be higher. Similarly, the

CFR could be overestimated (due to missing mild cases in the denominator) or

underestimated (due to the lag period for deaths to occur in ICU or from missed deaths).

2. The models do not adjust for potential seasonal effects, as these are complex. Disease spread

is likely to be initially dampened if Covid-19 arrives in the warmer part of autumn or if case

numbers remain low until late spring. Conversely, winter conditions would increase disease

spread. The timing in the modelling for disease spread in NZ, did not consider potential

super-spreading events. If these occurred it could truncate the timeline to the epidemic peak.

3. This analysis does not consider potential internal travel restrictions to prevent spread to

another island or region (eg, when there is uncontrolled spread in the North Island – then

protective sequestration could be applied to the South Island or of regions with limited road

access such as the West Coast). Eg, the pandemic plan for Iceland identifies where road

blocks would be set up to stop pandemic spread within the main island.

4. This analysis also does not consider protective sequestration of institutions such as retirement

villages (ie, this was done for some institutions in the 1918 pandemic with some success).

This intervention could impact on reducing the mortality burden since older people appear to

have higher CFRs.

5. While this analysis uses such estimates as “25%” and “50%” control – it is possible that even

more intense control is plausible eg, as per the above mentioned travel restrictions, protective

sequestration and various other social distancing options. Also mass media campaigns by the

government to promote staying at home when sick and to enhance hand and respiratory

hygiene, could also reduce transmission. Indeed, the recent decline in new cases in China

suggests that it is quite plausible that control measures could be more effective than the

“50%” used in the modelling (albeit with some of the control measures in China being

relatively severe).

6. Estimates for numbers of deaths assume people can receive necessary hospital and ICU

treatment, but as health services will potentially be over-burdened, the CFR is likely to

increase.

7. The models do not account for the potential discovery or development of more effective

treatments. This innovation could reduce the number of cases needing hospitalisation and

intensive care, and consequently reduce deaths.

7

Appendix 1: Case study of extrapolating from a large cruise ship outbreak of

Covid-19 to NZ (if this pandemic reaches NZ and becomes uncontrolled)

An advantage of considering the outbreak on the Diamond Princess cruise ship is that it provides a

clearer idea of the disease parameters and denominator populations – given the widespread testing of

the crew and passengers. While attempts at quarantine were made on the cruise ship, this does not

appear to have been fully effective and conditions on the ship are far more crowded than typical

community settings (and indeed the ship crew were in dormitories and continued to have contact

with passengers). Also the age distribution of passengers was probably older than the New Zealand

population – and so this may increase the risk of more severe outcomes, including death. On the

other hand, this cruise ship outbreak has been largely truncated via people being moved off the ship.

Also the calculations below make no allowance for how overloaded hospitals and ICUs may not

function normally when the pandemic peaks in a country like NZ, with the case fatality risk (CFR)

being likely to increase in overloaded settings.

Table A1: A case study using parameters from a large cruise ship outbreak of Covid-19 and

extrapolating to the NZ situation (if the pandemic reaches NZ and becomes uncontrolled)

Characteristic

Diamond Princess

(cruise ship) data

and estimates

Extrapolations

to NZ Comment

People with Covid-

19 infection

(including some

asymptomatic

infection)

634 people on 20

February 2020,8 so

this was 17.1% of

the 3711 (crew and

passengers)2

initially on the ship

847,000 We used this proportion of cases

(17.1%) for the NZ population (4.96

million population). Of note, however,

is that some testing on the cruise ship

may still be proceeding and so this

proportion may increase with time.

Another complexity in terms of final

interpretation of the outbreak size, is

that the denominator has started to

change with evacuations from the

ship. Of note is that those passengers

with lab-confirmed COVID-19 were

disembarked and transferred to an

isolation ward at healthcare facilities.

Severity at level

requiring ICU

2.1% 17,700 On 18 February when 454 cases were

reported on the cruise ship: “The

health ministry said 19 of the infected

people were in serious condition, with

some of them in intensive-care

units.”9 So for this calculation we

assumed half of these 19 people were

in ICU.

Estimated deaths

(extrapolating from

ICU numbers)

5* 6050 We used the 34.1% from the typical

case fatality in ICUs for severe

respiratory conditions10 (and for extra

context and workings see: Wilson et

al6).

8

Characteristic

Diamond Princess

(cruise ship) data

and estimates

Extrapolations

to NZ Comment

Estimated deaths

using an out-of-

China CFR

5* 6320 Out-of-China CFR as per a WHO

report on 20 February (8/1073 =

0.75%.)7

* Indeed, three people from the cruise ship have actually died as detailed in a 24 February WHO Situation Report (where

the number of cases/people testing positive had reached 695).

9

Appendix 2: Estimating the basic reproduction number (R0) for COVID-19 in

New Zealand

The basic reproduction number R0 represents the average number of secondary infections per case in

a fully susceptible population. Future projections of clinical burden and impact are highly sensitive

to the value of R0 selected for modelling as this value drives the proportion of the population

infected. Estimating the value of R0 is challenging, however, because estimates of R0 are themselves

sensitive to both measurement error and environmental conditions, particularly during an evolving

pandemic. Measures of R0 in a given population will strongly reflect the sociodemographic

characteristics, public health interventions, climate, and geography of that population.5 11

Because there have been relatively few cases outside China (and none to date in New Zealand), most

of the currently available estimates are based on cases occurring in China during the early stages of

the epidemic. Estimates of R0 calculated in this way vary from 2 to 5.12

R0 is consistently overestimated in the early phases of a pandemic, for a range of reasons.13 Taking

this bias into account together with contextual differences, it is highly likely that the true R0 in New

Zealand would be substantially lower than estimates from Chinese studies. Reasons for the

difference include:

• Higher levels of crowdedness in Chinese cities compared with New Zealand

• Initial overestimation of R0 e.g. because of changes in reporting rates.12

• Greater population susceptibility to infection from existing compromised respiratory health

due to poor air quality from air pollution and high smoking rates in China.

Table A2 shows estimates of R0 in the New Zealand population during respiratory virus pandemics.

Values of R0 have been consistently in the range 1 to 2, with one slightly higher early estimate.

Table A2. Estimates of the reproduction number in New Zealand during previous respiratory

virus outbreaks.

Estimated R0 (95% CI) Pandemic Source Comment

1.96 (1.80 – 2.15) 2009 H1N1 Nishiura et al., 200914 Preliminary estimate

1.25 (1.07 – 1.47) 2009 H1N1 Roberts et al., 201115 Updated estimate

1.55 (1.16 – 1.86) 2009 H1N1 Paine et al., 2010 16 Peak value

1.34 (1.27 – 1.38) 2009 H1N1 Opatowski et al., 201111 Confirmed cases

1.2 – 1.8 1918 pandemic Wilson et al., 201217 In community settings

These New Zealand estimates are also consistent with global estimates of R0 from previous

pandemics; retrospective estimates of R0 from systematic reviews and meta-analyses include:

• 1.2 to 1.8 in eight Southern Hemisphere countries for 2009 H1N1 pandemic influenza11

• 1.65 (IQR 1.53 – 1.70) in the 1957 influenza pandemic5

• 1.80 (IQR 1.47 – 2.27) in the 1918 influenza pandemic.5

During its early stages, Severe Acute Respiratory Syndrome (SARS) was estimated in modelling to

have an R0 of 2.2 – 3.6, but data from the first 205 probable cases in Singapore indicated an R0 < 1

10

from approximately the third week onwards.18 Mercer et al list several similar examples of this

trend.13

Summary

Currently available estimates of R0 are uncertain because the transmission characteristics of this

novel virus are still somewhat unclear. The “plan for” value of R0 = 2.2 indicates the current

consensus around scenarios for planning. However, estimates generated by data from China are

likely to introduce substantial overestimation of impacts in the New Zealand population. For this

reason, scenarios based on a lower value of R0 are also presented for comparison purposes. The value

of R0 = 1.5 included in the models presented here is consistent with a) the lower limit of estimates

from China, b) estimated R0 in other New Zealand pandemics, and c) global estimates from other

pandemics that were generated after the earliest stages of the event.

11

Appendix 3: Potential age and ethnic distribution of Covid-19 health impacts

(assumed uncontrolled spread occurs in NZ).

Key messages

• The numbers presented here represent potential future scenarios rather than predictions

and will be strongly influenced by the transmissibility and severity of the Covid-19

pandemic when it reaches New Zealand.

• If Covid-19 follows the same patterns as previous pandemics, there may be a relatively high and

heavily unequal hospitalisation and mortality burden on Māori and Pacific populations.

• Elderly are particularly at risk, and Māori and Pacific elderly even more so, and from younger

ages.

• Nevertheless, these poor outcomes are modifiable ie, if the entry of Covid-19 to NZ is delayed, if

there are successful interventions that substantially reduce transmission in Māori and Pacific

communities (eg, intensive hand and respiratory hygiene messages and social distancing

interventions such as reducing public gatherings and closing schools) or even more radical

measures are used such as protective sequestration of communities.

The numbers in this Appendix are based on the “plan for” scenario of R0=2.2, 48hrs pre-

symptomatic transmission, with 25% effective infection control measures. We use the Chinese data

(CCDC) 2% case fatality risk3 as our basis for age-specific deaths, and we treat the age distribution

of cases in that report as the age distribution of hospitalisations.

Ethnic distribution

We assume ethnic inequalities in mortality will be similar to those in the 2009 Influenza A(H1N1)

pandemic, ie, that the risk of hospitalisation was 5 times higher for Māori and 7 times higher for

Pacific peoples,19 while the risk of death was 2.6 times higher for Māori (95%CI: 1.3 – 5.3) and 4.6

times higher for Pacific peoples (95%CI: 2.0 – 7.2) than for NZ European/Other.20

The estimated ethnic distribution of the hospitalisation and mortality burden under the “plan for”

scenario is shown in Table A3-1. Numbers do not include any age-standardisation.

Table A3-1. Estimated ethnic distribution of Covid-19 hospitalisations and deaths, “plan for”

scenario.

Ethnic group Hospitalisations Deaths

n Pop % n Pop%

Māori 128,230 16.4% 9,240 1.2%

Pacific 92,190 23.1% 8,360 2.1%

NZ European/Other 115,580 3.3% 16,000 0.5%

Total 336,000 6.8% 33,600 0.7%

Age distribution

Our estimates of the age distribution of deaths (Table A3-2) are based on CCDC reports of case

fatality risk by age, and the Chinese population age structure estimated from the CIA World

Factbook, adjusted to the New Zealand population and the projected number of deaths.

12

Table A3-2. Estimated age distribution of Covid-19 mortality, “plan for” scenario.

Age group (years) Hospitalisations Deaths

n Pop % n Pop %

0-9 3,132 0.5% 0 0.0%

10-19 4,173 0.7% 19 0.0%

20-29 21,193 3.0% 102 0.0%

30-39 47,807 7.4% 282 0.0%

40-49 43,689 7.0% 482 0.1%

50-59 65,690 10.4% 2,122 0.3%

60-69 63,542 12.2% 5,689 1.1%

70-79 41,181 11.9% 8,155 2.3%

80+ 45,593 25.2% 16,750 9.3%

Total 336,000 6.8% 33,600 0.68%

Age and ethnic distribution

The age and ethnic disaggregated figures which follow are based on multiple interpolations and

extrapolations. As these numbers are a number of steps removed from their bases, they can only be

very rough estimates. Numbers for Pacific peoples over the age of 60 were particularly unstable. The

key message from these numbers is that the mortality burden is likely to fall particularly heavily on

older (aged ~60+) Māori and Pacific peoples

NZ European/Other

Estimated NZ European/Other deaths under the “plan for” scenario are shown in Table A3-3.

Table A3-3. Estimated NZ European/Other age distribution of Covid-19 hospitalisations and

mortality, “plan for” scenario.

Age group (years) Hospitalisations Deaths

n Pop %* n Pop %

0-9 1,021 0.2% 0 0.0%

10-19 1,353 0.3% 8 0.0%

20-29 6,666 1.4% 42 0.0%

30-39 13,426 3.5% 104 0.0%

40-49 14,137 3.3% 205 0.0%

50-59 23,265 4.9% 987 0.2%

60-69 23,382 5.7% 2,748 0.7%

70-79 16,539 5.6% 4,300 1.4%

80+ 15,788 11.8% 7,615 5.7%

Total 115,580 3.3% 16,009 0.46%

* Percentages are row percentages (with the denominator being the population in that age-group).

Māori and Pacific peoples

There are difficulties extrapolating mortality distributions from the Chinese example to the Māori

and Pacific populations, who not only have a very different population age structure, but also have

different life expectancies. Comparing population mortality rates, in ordinary circumstances, the

mortality risk of a Māori person aged 60 years, for example, is roughly equivalent to the mortality

risk of a NZ European person aged 70 years.

13

Since Covid-19 mortality appears to heavily reflect age-related vulnerability, we have calibrated

Māori and Pacific age-specific mortalities to NZ European/Other age-specific mortality when

calculating estimated ethnic-group specific age-related mortality. Calibrating age bands reduces the

starting age of the upper “80+ years” age band to 74 years for Māori and 73 years for Pacific

peoples.

Following this calibration, estimates for age-related mortality for Māori and Pacific peoples under

the “plan for” scenario are shown in Tables A3-4 and A3-5. In Table A3-5 the 60+ and 73+ age

bands were combined, as low population numbers in the top band resulted in an unstable estimate.

Table A3-4. Estimated Māori age distribution of Covid-19 hospitalisations and mortality, “plan

for” scenario.

Age group (years) Hospitalisations Deaths

n Pop % n Pop %

0-9 2,475 1.5% 0 0.0%

10-19 3,030 1.9% 10 0.0%

20-29 17,305 13.5% 68 0.1%

30-39 17,945 20.8% 144 0.2%

40-49 25,404 30.7% 598 0.7%

50-59 27,412 36.0% 1,788 2.3%

60-73 21,472 35.0% 3,098 5.0%

74+ 13,186 74.3% 3,529 19.9%

Total 128,230 16.4% 9,236 1.2%

Table A3-5. Estimated Pacific age distribution of Covid-19 hospitalisations and mortality,

“plan for” scenario.

Age group (years) Hospitalisations Deaths

n Pop % n Pop %

0-9 1,949 2.1% 0 0.00%

10-19 2,266 2.8% 10 0.0%

20-29 14,393 20.2% 76 0.0%

30-39 14,760 30.5% 158 0.3%

40-49 18,330 45.2% 575 1.4%

50-59 17,935 53.0% 1,559 4.6%

60+ 20,099 59.8% 4,739 14.1%

Total 92,190 23.1% 8,355 1.2%

14

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